212 results about "Temporal consistency" patented technology

The invention relates to the technical field of storage equipment control, and discloses a dynamic cooperative control method and system for digital twin-driven storage equipment. The method comprises the following steps: firstly, constructing a storage equipment digital twin model which covers a storage space three-dimensional model, equipment motion parameters and a real-time operation state; and then collecting sensor data in real time, and generating a collaborative operation path through a dynamic path planning algorithm containing conflict detection and priority allocation. And correcting the equipment motion track and updating the model state by using the space-time consistency verification model, finally inputting the corrected track into an equipment control instruction generation module, and outputting a driving signal to control the storage equipment. The system comprises a digital twinborn model construction module, a sensor data acquisition module, a collaborative operation path generation module, a motion trail correction module, a control instruction generation module, a driving module and the like. The storage space utilization efficiency and the equipment collaborative operation capability are improved, real-time monitoring and fault prevention are achieved, and intelligent upgrading of a storage system is promoted.

The invention discloses a casting surface treatment defect detection and quality evaluation method and system, and relates to the technical field of casting quality evaluation, and the method comprises the steps: collecting the surface data of a to-be-detected casting, and carrying out the data preprocessing, and obtaining a standardized input data set and a standardized data subset; calling a corresponding analysis sub-model for each subset, outputting quality features and confidence coefficients, and summarizing the quality features and the confidence coefficients into a sub-source result; environment state information is acquired to determine sub-model weights, and weighted fusion is carried out on sub-source results to obtain an evaluation result and an overall confidence coefficient; calculating space / feature / time consistency and judging according to a combination rule; when a re-checking condition is met, obtaining a re-checking label backflow updating data set, and training an updating model and parameters; and dynamically adjusting the threshold value and the weight value according to the performance index for subsequent evaluation. According to the method, multi-source data and environment information can be fused, weight self-adaption and closed-loop updating are parallel, accuracy and stability are improved, misjudgment and missed judgment are reduced, and complex working condition adaptability and long-term reliability are enhanced.

The invention relates to the technical field of software time synchronization, and discloses a software time synchronization method and system for multi-sensor data fusion, and the method comprises the steps: extracting temperature, load and drift frequency characteristics through principal component analysis based on the working state and historical drift data of a sensor, and constructing a confidence evaluation model to calculate the credibility of a timestamp; identifying an abnormal node group by using k-means and an isolated forest algorithm, analyzing a phase deviation fluctuation and network delay interaction effect, extracting a nonlinear drift feature in combination with a Prophet algorithm, and calculating a phase correlation value by using Hilbert cross-correlation; and dynamically adjusting node clock parameters and generating a calibration timestamp according to the network influence weight and the stability evaluation result. According to the method, the problem of time desynchrony caused by clock drift and network delay factors in a distributed system is effectively solved, and the overall time consistency and reliability of the system are improved.

The invention relates to the technical field of information auditing management, in particular to a personnel archive information extraction intelligent auditing method and system based on a large language model, and the method comprises the following steps: generating a timeline sequence and an attribute feature set which are separated through spatial-temporal feature decoupling; performing cross-modal alignment verification on the timeline sequence and the attribute feature set, and outputting a time-space consistency map with conflict marks; generating a risk conduction probability matrix by analyzing abnormal nodes; calculating timeline credibility, attribute credibility and association credibility to form a three-dimensional credibility vector; and automatically executing logic self-consistent repair or generating an audit report comprising a contradictory traceability path. According to the method, the traceability path and the influence weight of the data conflict are provided, a visual basis is provided for manual auditing, and the conflict judgment efficiency in a complex data environment is improved.

The invention discloses an intelligent report generation method and system based on multi-source heterogeneous data fusion, and particularly relates to the technical field of data fusion, and the method comprises the steps: constructing a fact anchor point containing a stable identifier in a fusion layer, and solidifying an aperture version signature and a time interval; packaging a screening condition, a sorting rule, an access plan, time window limitation and the like into a controlled context packet and freezing the controlled context packet into a unique fact source; in the range, generating sentences by using a limited template and writing sentence-level reference marks, so that each sentence can be traced back to a data slice and an evidence fragment; performing consistency verification according to a source, an aperture and time, performing conservative degradation on the attribution expression according to a consistency threshold value, and outputting a degradation strategy description; and finally, page two-way pointer and cross-sentence consistency recording and audit playback are realized through an evidence chain pointer table and a published version signature, and the problems of caliber drift, cross-window access and difficulty in evidence tracing are solved.

The invention belongs to the technical field of robot learning, and particularly discloses a robot continuous imitation learning method and system based on a double diffusion model. Comprising the following steps: generating old task data in a submerged space by utilizing a pre-trained latent diffusion model, generating a model through guidance of a task identifier in combination with conditional diffusion, generating an image and state data related to an old task, modeling to construct a diffusion model, realizing multi-modal mapping from a state to an action, and generating a diversified action sequence; high-quality images and state data are generated through a pre-trained variational auto-encoder and a conditional diffusion model, and the consistency of the generated data in time and the accuracy of global semantics are ensured through time sequence continuity constraint and semantic consistency constraint; and performing feedback optimization training on the latent diffusion model. According to the method, the loss weight is dynamically adjusted, and the model is ensured to show good generalization ability and stability in a complex task in combination with collaborative training of an imitation learning strategy and a generator.

The invention discloses a multi-unmanned aerial vehicle cluster cooperative positioning method based on multi-sensor fusion, and the method comprises the following steps: S1, collecting and processing the multi-sensor data of unmanned aerial vehicles, compensating VIO accumulated drift through fusing ultra-wideband ranging information, guaranteeing the time-space consistency of the multi-sensor data through employing a layered time synchronization strategy, and carrying out the collection and processing of the multi-sensor data of the unmanned aerial vehicles; suppressing multipath interference of the UWB signal and eliminating an NLOS error by using adaptive Kalman filtering, and obtaining initial positioning data of the unmanned aerial vehicle; s2, acquiring key frames of multiple unmanned aerial vehicles based on a window sampling method, and performing key frame optimization and outlier processing to balance noise suppression and real-time performance; and S3, constructing a factor graph model of the key frame data based on a graph optimization theory, and obtaining optimized cluster position information by solving a nonlinear least square optimization problem. The method effectively improves the accuracy and stability of multi-unmanned aerial vehicle cluster cooperative positioning, and can be applied to the fields of unmanned aerial vehicle logistics distribution, environment monitoring and the like.

The invention discloses an MVPS video coding method and device based on deep learning, and a medium, and relates to the technical field of video coding. The method comprises the following steps: acquiring a video stream, performing time domain and space domain feature extraction on the video stream through a feature extraction unit, and generating a joint feature map through an adaptive weight fusion layer; carrying out bidirectional feature interaction between hierarchies on the combined feature map to obtain a multi-scale feature set; generating a frame-level quantization parameter initial value according to the multi-scale feature set in combination with current network bandwidth feedback; performing time sequence consistency correction on the quantization parameter initial value to generate a target quantization parameter; and performing block coding on the current frame according to the target quantization parameter to generate a coded data stream. According to the method, through dynamic parameter adjustment, hardware algorithm collaborative optimization, cross-frame consistency enhancement and network awareness, the problems of low efficiency, insufficient real-time performance, unstable quality and poor adaptability of a traditional scheme are solved.

The invention relates to the technical field of computers, and discloses a time series data prediction method and device based on a diffusion model, equipment and a medium, and the method can ensure the consistency of different modal data in time by performing time dimension alignment on target time series data and multi-modal evaluation data. And feature splicing is performed on the multi-modal data corresponding to each time step, so that the multi-modal information can be integrated, and the data features of each time step are enriched. And on the basis of a self-attention mechanism, weighted summation is carried out on the fused feature vectors to obtain a self-attention feature matrix, so that mutual correlation between a time sequence dependency relationship and multi-modal features in a feature sequence can be captured, and richer feature representation is generated. And diffusion simulation is carried out on the self-attention feature matrix by using the pre-training diffusion model, more accurate target prediction data is output, and the accuracy of a prediction result is improved. And the application efficiency of the diffusion model in time series data analysis scenes in the financial field and the medical field is expanded.

Systems and methods are disclosed that utilize a triplane fusion architecture that achieves both state-of-the-art three-dimensional (3D) reconstruction accuracy and temporal consistency. For instance, recognizing the need to maintain both coherent identity and dynamic per-frame appearance to enable the best possible realism, the triplane fusion architecture may include an undistorter that removes view-dependent distortions from the raw triplane by using the triplane prior as a reference and a fuser that fuses the triplanes together to recover the occluded areas in the input frame by incorporating features from the triplane prior that is lifted from the frontal reference image. By using the undistorter, the fuser, and a volume renderer, the triplane fusion architecture allows for reconstruction of 3D portrait models from a monocular video stream of a user, which may be useful in various applications including 3D telepresence.

The invention relates to the technical field of mechanical arms, in particular to a man-machine interaction system of an intelligent mechanical arm with a body. The system decomposes a long time sequence task into sub-tasks through a large language model, and introduces a task acquisition module, a task queue management module, a task re-planning module and a control execution module, uses visual detection to identify gestures and environment events to trigger temporary tasks, and maintains interruptible marks and safety anchor points based on priorities and interruption risks. And during interruption, the mechanical arm with the timestamp and the environment state are collected, during recovery, the states are compared, local re-planning is carried out, transition sub-tasks are automatically generated for failure sub-tasks, and the mechanical arm is controlled to execute after constraint verification. According to the system, on the premise that the structure consistency, the space consistency and the time consistency are guaranteed, safe interruption and efficient recovery in a long-time-sequence task can be achieved, and the autonomy, the real-time performance and the operation safety of the mechanical arm in a man-machine cooperation scene are remarkably improved.

The invention discloses a cross-chain meteorological and hydrological data consistency verification method oriented to multi-block-chain collaboration, which effectively solves the problem of data alignment caused by inconsistent time standards in a multi-chain environment by collecting multi-source data and generating a data tuple with a geographic identifier and distributing a unified reference timestamp for the data in combination with a physical clock synchronization mechanism. And furthermore, the time deviation is extracted through a layered verification structure and inter-chain cross check, and the timestamp weight is dynamically adjusted, so that the time synchronization precision of the cross-chain data is improved. Finally, a standardized time sequence is generated through segmented aggregation of the data, cross-chain storage is completed, regular backtracking verification is performed in combination with a supervision chain, time consistency and content credibility of the meteorological and hydrological data in the cross-chain circulation process are guaranteed, and efficient collaboration and traceability between heterogeneous block chain systems are achieved.

The invention provides an intelligent traceless erasing method and system for video picture characters, and relates to the technical field of video stream processing, and the method comprises the steps: carrying out the space-time dual-domain feature extraction of a preprocessed first video frame sequence, and obtaining a first space-time feature; and establishing a space-time double-domain attention model according to the first space-time feature, the space-time double-domain attention model calculating fusion space domain and time domain features through cross attention, dynamically distributing fusion weights based on the character movement speed, obtaining a second video frame sequence of the to-be-processed video stream, preprocessing the second video frame sequence, and extracting a second space-time feature. Texture and motion characteristics of front and back frames are synchronously referenced through a space-time double-domain attention model, so that the time sequence coherence of a repaired area is improved, a time sequence consistency compensation mechanism driven by an SSIM is combined, the flicker frequency is reduced, and the time-space domain weight is adaptively adjusted according to the character speed through a dynamic weight distribution strategy, so that the time sequence coherence of the repaired area is improved. And stable erasing and rapid moving of characters are realized.

The invention provides a video deblurring method based on a memory diffusion network. The core of the method comprises a deblurring branch and a diffusion branch. The deblurring branch has a bidirectional circulation structure, maintains a simple and effective memory bank containing a specific updating mechanism, and can effectively utilize the space-time relationship between video frames to further ensure the time consistency between the frames in the final deblurred video. The diffusion branch comprises a lightweight diffusion network to generate multi-scale diffusion prior features. According to the method, the effect of an existing regression model acting on video deblurring is improved by combining the diffusion prior generated by the diffusion network. A simplified and effective memory bank is constructed to screen and store time frame information to accurately capture a complex space-time local relationship in a fuzzy video so as to realize deblurring with finer granularity, and a specific memory updating algorithm is designed to refresh the memory bank. The space-time information between continuous video frames is better utilized, and the time consistency between the frames is ensured.

The invention provides a face video depth forgery detection method based on face micro-expressions, which comprises the following steps of: 1, extracting face micro-expression actions from a face video to obtain a micro-action time sequence; step 2, performing time sequence consistency learning according to the micro-action time sequence; step 3, based on a GRU network architecture, introducing an attention mechanism, and constructing an authenticity detection model for capturing abnormal features of the micro-action time sequence under a short scale; step 4, training the authenticity detection model; and step 5, carrying out authenticity detection, extracting a human face micro-motion track for a to-be-detected human face video with unknown authenticity, and then detecting time sequence abnormal features by adopting the authenticity detection model trained in the step 4 to obtain a classification result of whether the to-be-detected human face video belongs to a forged video or not. The method is competitive in accuracy and generalization.

The invention provides a multi-sensor fusion robot grabbing path planning method and device, and relates to the field of automation. The method comprises the following steps: constructing a first time correction relation based on observation parameters of a multi-source sensor; correcting the relation based on the first time; inputting the hardware timestamp synchronized to the master clock source into the extended state estimation model; iteratively updating the observation parameters of the multi-source sensor based on the extended state estimation model, and constructing a second time correction relation according to the updated observation parameters of the multi-source sensor; constructing a pose continuous time trajectory function corresponding to the target robot based on the second time correction relation; the continuous time trajectory function and the multi-source sensor observation parameters serve as input, and a grabbing path corresponding to the target robot is output through a time sequence consistency constrained joint optimization model; and controlling the target robot to execute grabbing operation based on the grabbing path. The problem that in the prior art, the precision of a robot grabbing path is low is solved.

The invention relates to the technical field of computer vision, in particular to a space-time consistency data generation method for visual target tracking. The method comprises the following steps: firstly, training a path generator on a target tracking training set, learning a motion law of a target in a time sequence by using optical flow estimation and conditional variation coding technologies, and generating a target motion track conforming to physical constraints; and then, based on the generated target trajectory, introducing a space-time consistency attention mechanism to guide a text-video generation model, and under the condition of keeping basic model parameter freezing, constraining the position, scale and continuity of a target in a generation frame through an attention network, thereby synthesizing a video frame sequence with real motion features. According to the method, target tracking video data with real motion characteristics and high time sequence consistency is generated, and the robustness of the model to complex motion, illumination change and shielding conditions can be improved in different scenes.

The invention discloses a multi-agent cooperation method based on hierarchical reinforcement learning of a structural information theory, and the method comprises the steps: carrying out the hierarchical representation of environment observation through a directed structural entropy optimization technology, mapping a high-dimensional original state into a low-dimensional abstract space, eliminating noise interference, and capturing a direction-sensitive state transition mode; on the basis, a multi-scale skill tree is constructed in combination with time sequence characteristics and a structural information theory, and time-space consistency optimization of a long-range strategy is achieved. And finally, expanding to a multi-agent scene, and improving the group decision-making efficiency through dynamic role division and a cooperation framework. The invention aims to solve the key bottleneck of traditional hierarchical reinforcement learning in a multi-agent cooperation complex dynamic environment through dynamic hierarchical abstraction, multi-scale skill discovery and a group cooperation mechanism, and improve the precision and efficiency of multi-agent cooperation.

The invention relates to the technical field of intelligent education, in particular to an interactive English teaching method and system based on AI vision, and the method comprises the steps: obtaining the voice and mouth dynamic image frame segments of a student, extracting an offset frame segment to generate a semantic motion disjunction interval, analyzing the time sequence consistency of a behavior signal and a motion cause word triggering frame segment, and calculating the motion matching degree. Time synchronism of the speech output and the visual action is evaluated. According to the invention, through real-time analysis of the voice and mouth dynamic image of the student, the alignment offset of the pronunciation and the mouth action is identified, and in combination with behavior signals such as eye fixation and head rotation of the student, the interactivity between the language action and the behavior of the learner is comprehensively evaluated, so that the efficient alignment of the language expression and the standard template is ensured; by analyzing the distribution frequency and the matching degree of the voice behaviors, a personalized teaching path is optimized. In addition, delay frames and contour offset are deeply analyzed, the synchronism of voice and visual actions is enhanced, and the real-time feedback and interaction effect in the learning process is improved.

The invention provides a multi-modal temperature control method and system for a three-in-one laminating plastic packaging machine, and the method comprises the steps: carrying out the synchronous collection of data of a temperature sensor, a pressure sensor and a visual sensor through a timestamp alignment technology, and guaranteeing the time sequence consistency of multi-modal data; original data of the temperature and pressure sensor are processed through a Gaussian filtering algorithm, high-frequency noise is filtered out, and smoothed sensor data are obtained; aiming at image data acquired by a visual system, a self-adaptive illumination compensation algorithm is adopted to eliminate the influence of environment illumination change on adhesive film detection; in the reflective area of the adhesive film, an image is preprocessed by using a polarization filtering technology, so that reflective interference is reduced, and the accuracy of visual detection is improved; inputting the filtered temperature and pressure data and the processed visual data into a preset data fusion model to generate a multi-modal feature vector; and optimizing the fuzzy control rule through a genetic algorithm, and generating an optimal control strategy by taking historical data of temperature and pressure as training samples.

Systems and methods are disclosed related to a 3D grounded video foundation model. A video generation method and system provide 3D conditioning information to a video diffusion model to improve generated video quality (object and temporal consistency) that is grounded in three dimensions (3D). The video generation method and system also enable precise camera control, cinematic effects, and scene editing. Video output corresponding to a set of camera specifications is generated for a scene from input image(s) including one or more images of a static scene or a sequence of images (video) for a dynamic scene. The input image(s) are used to calculate a 3D cache representing the scene. The 3D cache is rendered according to the set of camera specifications to produce a frame sequence and a mask sequence that identifies missing pixels in each frame. The frame sequence is encoded and masked to generate the output video.

The invention discloses a method and a system for generating a long video with a controllable camera track based on a video diffusion model. Comprising a camera track and initial frame preparation stage, a point cloud construction and multi-view image generation stage, a scale factor alignment optimization stage, a camera motion prior injection and noise initialization stage, a diffusion inversion generation stage and a sliding window time consistency fusion stage. Three-dimensional camera track modeling, projection reconstruction and diffusion processes are combined, a video content generation process is explicitly guided to be aligned with a track path set by a user, and long video generation which is reasonable in structure, natural in vision and continuous in time is achieved.

The invention discloses a time-space consistency-based video super-resolution method and device for a complex operation scene, and a medium, belongs to the field of image and video processing, and aims at solving the problem that a stable, clear and continuous-structure video sequence is difficult to generate in an existing method, and the time-space consistency-based video super-resolution method for the complex operation scene based on video prior is provided. Comprising the following steps: utilizing submerged space modeling, mapping an input low-resolution video to a submerged space, and obtaining a corresponding submerged variable representation; initializing a random noise tensor with the same size as the latent variable expression as an initial noise state; in each step of sampling, dividing a noise state and latent variable representation into a plurality of blocks according to space and time dimensions; denoising is carried out on each tile block; the initial noise is converted into high-quality latent variable representation; and a high-resolution video is reconstructed. According to the method, the power inspection video with high resolution and high consistency can be generated, the generation stability is kept, and the video texture detail expression capability is improved.

The embodiment of the invention discloses a multi-process-based real-time multi-path camera synchronization method and system, according to the embodiment of the invention, frames with the same time are paired according to timestamps, real-time unified processing of multi-path incoming frames is realized through a shared memory circular buffer area, each camera acquires the frames in an independent process and attaches the timestamps, and the real-time synchronization of the multi-path cameras is realized. And interacting data through a shared memory circular queue, and dynamically matching frames according to a timestamp by a synchronization process. Compared with an existing scheme, data interaction real-time performance is improved through shared memory zero copy, synchronization precision is improved through timestamp matching and dynamic error control, expansibility is enhanced through a multi-process parallel framework, the precision and real-time performance of multi-camera data synchronization are greatly improved, and the method is suitable for large-scale popularization and application. The requirement of three-dimensional reconstruction for multi-view data time consistency is met through targeted optimization, high efficiency and accuracy of subsequent three-dimensional reconstruction and data processing are ensured, and synchronization precision and real-time performance are remarkably improved.

The invention belongs to the technical field of computer vision and human body motion capture, discloses a time sequence smooth human body motion capture method and system based on calorific value map guidance, and aims to solve the defects of an existing method in the aspects of time sequence consistency and structural stability. The method comprises the following steps: receiving a sequence containing RGB video frames, extracting a 2D attitude and generating a calorific value map; time sequence characteristics of RGB and the calorific value map are fused through a double-branch time sequence attention block (DBTA), and time sequence action clues are captured; an intra-frame attitude reasoning (IPR) module is provided, and a graph attention mechanism is introduced to enhance structural relation modeling of single-frame intra-frame joint points so as to ensure structurally coherent attitude prediction. According to the method, the precision and the time sequence smoothness of three-dimensional human body posture estimation are remarkably improved.

The invention relates to the technical field of trajectory control, in particular to a multi-modal motion trajectory real-time tracking method and system for machine control, which performs weight redistribution through channel noise variance and amplitude difference, performs weighted stacking and continuous updating in combination with Kalman filtering, reduces state estimation variance and channel bias transfer, and improves the tracking accuracy of a multi-modal motion trajectory. Time consistency and amplitude consistency of fusion track state data are improved, an overshoot peak value is inhibited, a deviation attenuation process is shortened, abnormal samples in a deviation sequence are rejected through an isolated forest, interference of outliers on parameter updating is reduced, and speed difference and acceleration difference are compared for a correction control sequence, so that the accuracy of correction control is improved. The boundary detection rate and the determinacy of the stage number are improved, the energy change rate is screened through the speed difference and the curvature value of the adjacent stages, the transfer relation is determined, the time sequence coherence and the geometric continuity of stage switching are kept, and the time sequence stability and the stage connection coherence of the control instruction are kept in the multi-degree-of-freedom space.

The invention relates to a coal mine flame pseudo fire interference suppression method based on time sequence consistency analysis, and belongs to the technical field of coal mine safety monitoring. According to the method, aiming at the problem of high false alarm rate of a flame detection system caused by false fire source interference such as electric welding sparks and mechanical friction in a mine environment, the flame identification accuracy is improved through time sequence consistency analysis. According to the technical scheme, the method comprises the following steps: acquiring an infrared spectrum flame saliency probability graph of continuous image frames in an underground coal mine; extracting a flame candidate region by adopting Otsu adaptive threshold segmentation in combination with morphological optimization and hole filling; and performing time sequence consistency analysis on the continuous frame candidate regions, and calculating the spatial overlapping degree, the morphological stability and the infrared thermal intensity change trend of adjacent frames. According to the method, the false fire false alarm rate is remarkably reduced, the robustness to complex backgrounds and dynamic illumination is enhanced, the method can be independently integrated to an existing infrared flame detection system or used in cooperation with a deep learning model, and the reliability of mine fire monitoring is improved.

The invention provides a lightweight robust real-time video matting method and system, and relates to the technical field of video image processing. The invention provides a lightweight robust real-time video matting method and system. A lightweight encoder is applied to a video matting task; features and time information are aggregated by using a cyclic decoder, so that good time consistency is ensured; more edge details are reserved by using a wavelet down-sampling technology, and the matting precision is improved; in the reasoning process, a re-parameterization technology is used to improve the reasoning speed of the model, and a real-time video matting solution with good balance between precision and speed is realized. Meanwhile, the invention provides a lightweight robust real-time video matting system, which comprises a down-sampling module, an encoder module, a feature pyramid module, a cyclic decoder module, a refining module and a high-resolution prediction module, has a high-precision matting effect for a real-time video scene, and meets the real-time application requirement.

The invention discloses an internal control risk dynamic identification method and system based on a multi-modal model, and relates to the technical field of risk dynamic identification methods. According to the method, through an unsupervised state transition analysis mode, the hidden abnormal jump paragraph in the behavior mode can be dynamically identified, manual rule setting is not needed, and the self-adaptive capability of risk identification is enhanced; when a time lag problem exists in a multi-modal log, through operation chain backtracking and a virtual calibration mechanism, the time consistency and causal integrity of a cross-modal behavior chain are ensured, and the misjudgment probability caused by record inconsistency is remarkably reduced; a state sequence is mapped into a process topological graph by adopting a sliding matching method, free fragments which cannot be matched structurally are effectively identified, and starting and ending points of risk behaviors can be accurately positioned.

The invention provides a clock synchronization method, a multi-axis drive control system and a computer storage medium, and the method comprises the steps: obtaining a clock synchronization signal which is sent by a master station timer and has a matched period at a slave station end, selecting any timestamp in the clock synchronization signal as a reference point, and carrying out the clock synchronization based on the reference point, determining the corresponding position of the reference point in the counting signal of the local timer, mapping the time information of the master station to a local time axis of the slave station, and calculating the phase difference between the corresponding position of the reference point in the counting signal of the local timer and the starting position of the next period of the counting signal of the local timer, and quantizing the real-time deviation of the master and slave clocks, and finally, dynamically adjusting and updating the period of a counting signal of a local timer to eliminate the phase difference, so that the slave station clock period is kept synchronous with the master station, and the time consistency of the action of each slave station in the multi-axis driving system is ensured.